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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Ainet : Jurnal Informatika Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma, Logika dan Komputasi Jurnal Informatika Progres Mechatronics Journal in Professional and Entrepreneur (MAPLE) ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Muhyiddin A.M Hayat
Universitas Muhammadiyah Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Analisis Persentase Area Kerusakan Daun Bawang Menggunakan Model Hybrid Transformer Berbasis SegFormer-TransUNet Muliana; Rizki Yusliana Bakti; Muhyiddin AM Hayat
Arus Jurnal Sains dan Teknologi Vol 4 No 1: April (2026)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajst.v4i1.2673

Abstract

Penelitian ini bertujuan mengembangkan sistem segmentasi citra untuk menghitung persentase area kerusakan daun bawang menggunakan model hybrid transformer berbasis SegFormer-TransUNet. Permasalahan utama yang diangkat adalah keterbatasan penilaian visual manual yang cenderung subjektif, lambat, dan sulit diterapkan secara konsisten pada skala lahan yang luas. Data penelitian berupa citra daun bawang dan mask anotasi yang memisahkan area daun sehat, daun rusak, dan background. Tahapan penelitian meliputi akuisisi citra, preprocessing, pembentukan label mask, pelatihan tiga skenario model, evaluasi kuantitatif, serta perhitungan persentase kerusakan berbasis rasio piksel. Model hybrid dirancang dengan memanfaatkan SegFormer sebagai encoder untuk menangkap konteks global dan TransUNet sebagai decoder untuk merekonstruksi detail spasial. Hasil evaluasi menunjukkan bahwa model hybrid memperoleh accuracy 0,9590, mIoU 0,8333, dan Dice coefficient 0,8686. Nilai tersebut lebih tinggi dibandingkan SegFormer (accuracy 0,9543; mIoU 0,7671; Dice 0,8057) dan TransUNet (accuracy 0,9583; mIoU 0,8145; Dice 0,8509) pada metrik utama segmentasi. Temuan ini menunjukkan bahwa integrasi fitur global dan detail lokal mampu meningkatkan kualitas segmentasi serta menghasilkan dasar kuantitatif untuk estimasi tingkat kerusakan daun bawang.
Integrating multi-criteria decision making and public sentiment analysis for sustainable urban green space planning Muhammad Syafaat S. Kuba; Muhammad Faisal; Nurnawaty Nurnawaty; Titik Khawa Abdul Rahman; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Rizki Yusliana Bakti
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11168

Abstract

Sustainable planning of green open spaces (GOS) requires decision-making models that combine expert evaluation with public input. This study proposes a novel hybrid framework that integrates multi-criteria group decision making (MCGDM) with public sentiment analysis to support community-based and data-driven urban planning. The workflow consists of evaluating 25 community-proposed GOS locations using stepwise weight assessment ratio analysis (SWARA) for criteria weighting and MABAC-BORDA for multi-criteria ranking, resulting in 11 feasible alternatives. To incorporate community perspectives, a term frequency-inverse document frequency-support vector machine (TF-IDF–SVM) classifier was applied to 1500 public comments, where SVM achieved the highest accuracy (0.80–0.96). The integrated approach improves ranking stability, reduces decision ambiguity, and strengthens alignment between expert judgment and community sentiment. This study contributes a transparent, participatory decision-support model that unifies MCGDM and sentiment analysis to enhance the effectiveness of sustainable GOS planning.
Penerapan Watermark‎‎‎‎‎ Tak Terlihat pada Materi Pembelajaran ‎Digital Menggunakan QR Code‎ dan Least Significant Bit arikal khairat; Titin Wahyuni; Muhyiddin AM Hayat
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/hqt4ec35

Abstract

ABSTRAKPerkembangan bahan ajar digital meningkatkan risiko pelanggaran hak cipta dan pemalsuan konten, sehingga diperlukan mekanisme perlindungan yang tidak mengganggu tampilan visual. Penelitian ini mengimplementasikan watermark‎ing‎ tak terlihat dengan menggabungkan Quick Response (QR) Code sebagai pembawa informasi dan steganografi‎ Least Significant Bit (LSB) sebagai teknik penyisipan pada citra yang terdapat dalam dokumen. Sistem dikembangkan berbasis web dengan tiga modul utama: pembuatan QR Code‎, penyisipan watermark‎, dan validasi dokumen. Evaluasi dilakukan pada 10 dokumen berformat DOCX dan PDF dengan total 169 gambar. Kinerja imperceptibility diukur menggunakan Peak Signal-to-Noise Ratio (PSNR) dan Mean Squared Error (MSE). Hasil pengujian menunjukkan PSNR berada pada rentang 55,4–67,1 dB dengan MSE sangat rendah (0,02–0,19), menandakan kualitas visual citra tetap terjaga. Selain itu, seluruh watermark‎ berhasil diekstraksi (100%) dan QR Code‎ dapat dipindai tanpa kegagalan. Validasi integritas payload‎ secara opsional menggunakan CRC32 terbukti membantu memastikan keutuhan data yang disisipkan. Temuan ini menunjukkan bahwa kombinasi QR Code‎ dan LSB efektif, andal, dan efisien untuk melindungi bahan ajar digital dari penyalahgunaan tanpa menurunkan kualitas visual.Kata Kunci: Watermark‎ing‎ tak terlihat; QR Code‎; steganografi‎; Least Significant Bit; bahan ajar digital. ABSTRACTThe growth of digital teaching materials increases the risk of copyright infringement and content tampering, requiring protection mechanisms that do not degrade visual quality. This study implements an invisible watermark‎ing‎ scheme by combining Quick Response (QR) Code as the information carrier and Least Significant Bit (LSB) steganography for embedding within images contained in documents. A web-based system was developed with three core modules: QR Code‎ generation, watermark‎ embedding, and document validation. The evaluation used 10 DOCX and PDF documents comprising 169 images. Imperceptibility was assessed using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Experimental results indicate PSNR values of 55.4–67.1 dB with very low MSE (0.02–0.19), confirming that visual quality is preserved. All embedded watermark‎s were successfully extracted (100%), and the QR Code‎s remained fully scannable without failure. Optional payload‎ integrity checking using CRC32 further ensured the correctness of embedded data. Overall, the proposed QR Code‎–LSB combination provides a reliable and efficient approach to protect digital teaching materials against misuse while maintaining visual fidelity.Keywords: Invisible watermark‎ing‎; QR Code‎; steganography; Least Significant Bit; digital teaching materials.
Penerapan Natural Language Processing dan Regular Expressions dalam Validasi Artikel Ilmiah Fatimah Azzahra; Desi Anggraeni; Muhyiddin AM Hayat
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/dkjw7933

Abstract

ABSTRAKPenelitian ini membahas penerapan Natural Language Processing (NLP) dalam proses validasi otomatis aturan penulisan artikel ilmiah pada AINET: Jurnal Informatika. Sistem dikembangkan dalam bentuk aplikasi web interaktif yang bertujuan untuk memeriksa kesesuaian struktur dan format artikel ilmiah secara otomatis, meliputi penulisan judul, identitas penulis, abstrak, kata kunci, bagian wajib artikel, serta format referensi yang digunakan. Metode yang diterapkan dalam penelitian ini adalah pendekatan rule-based, yang terdiri dari beberapa tahapan, yaitu ekstraksi teks dari dokumen, preprocessing NLP, segmentasi dokumen, serta pencocokan pola teks menggunakan Regular Expressions (Regex). Evaluasi sistem dilakukan menggunakan metode black-box testing terhadap 20 artikel jurnal sebagai data uji untuk mengukur tingkat ketepatan hasil validasi yang dihasilkan sistem. Hasil pengujian menunjukkan bahwa sistem mampu mencapai tingkat akurasi sebesar 90%, di mana 18 artikel berhasil divalidasi sesuai dengan kondisi sebenarnya. Temuan ini menunjukkan bahwa integrasi NLP dan Regex efektif dalam mendukung proses validasi penulisan artikel ilmiah secara efisien, cepat, dan konsisten. Namun demikian, sistem masih memiliki keterbatasan dalam mendeteksi abstrak bilingual, konsistensi penggunaan bahasa pada header, serta variasi format referensi, sehingga diperlukan pengembangan lanjutan untuk meningkatkan keandalan dan fleksibilitas sistem.Kata Kunci: Natural Language Processing, Regular Expressions, Validasi Penulisan, Artikel Ilmiah, AINET: Jurnal InformatikaABSTRACTThis study discusses the application of Natural Language Processing (NLP) in the automatic validation of scientific article writing rules in AINET: Journal of Informatics. The system was developed as an interactive web-based application aimed at automatically checking the conformity of article structure and formatting, including the title, author information, abstract, keywords, mandatory article sections, and references. The method used in this study is a rule-based approach, which consists of several stages, namely text extraction, NLP preprocessing, document segmentation, and pattern matching using Regular Expressions (Regex). System evaluation was conducted using the black-box testing method on 20 journal articles to measure the accuracy of the validation results. The testing results show that the system achieved an accuracy rate of 90%, with 18 articles successfully validated in accordance with actual conditions. These findings indicate that the integration of NLP and Regex is effective in supporting the validation process of scientific article writing in an efficient, fast, and consistent manner. However, the system still has limitations in detecting bilingual abstracts, language consistency in headers, and variations in reference formats, indicating the need for further development to improve system reliability and flexibility.Keyworsds: Natural Language Processing, Regular Expressions, Writing Validation, Scientific   Articles, AINET: Journal of Informatics
KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT Jihan Izzathul Mujidah; Rizki Yusliana Bakti; Lukman; Muhammad Faisal; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Pepper fruit (Piper nigrum L.) is an agricultural commodity whose market value strongly depends on its ripeness level at harvest. Ripeness determination, which is still commonly performed through visual observation, tends to be inaccurate and subjective. This study aims to classify the ripeness level of pepper fruit based on skin color using an ensemble learning approach. The dataset consists of 1,996 pepper fruit images categorized into four ripeness levels unripe, semi ripe, ripe, and overripe. Color features were extracted from the HSV color model using color moment statistics including mean, standard deviation, and skewness. Random Forest and XGBoost models were combined using a soft voting method. The results show that the ensemble model achieved 98.25% accuracy, 98.30% precision, 98.27% recall, and 98.26% F1-score. The ensemble approach proved superior to single models by providing more accurate and stable classification of pepper fruit ripeness.
KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM Sarina; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study presents a classification model for detecting diseases in patchouli (Pogostemon cablin Benth) leaves using image processing techniques. The method combines Grey Level Co-occurrence Matrix (GLCM) for texture feature extraction and Support Vector Machine (SVM) for classification, optimised using the Particle Swarm Optimisation (PSO) algorithm. A total of 2,080 leaf images were collected and categorized into four classes: healthy, leaf spot, yellowing, and mosaic. Each image was augmented and converted to grayscale to enhance the dataset and reduce computational complexity. Four GLCM features—contrast, correlation, energy, and homogeneity—were extracted to represent leaf textures. The classification model achieved an accuracy of 89.74% using SVM alone, and improved to 97.12% when optimized with PSO. The results indicate that the integration of GLCM, SVM, and PSO provides an effective and accurate solution for early detection of patchouli leaf diseases, potentially supporting farmers in decision-making and improving crop productivity and quality.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN HYBRID SENTENCE-TRANSFORMERS DAN K-MEANS UNTUK PERBANDINGAN JURNAL Muhammad Asygar Faeruddin; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study addresses the challenge of identifying semantic relatedness between scientific journal articles by developing a classification system based on deep learning. The system applies an unsupervised learning approach using the Sentence-Transformers model and K-Means clustering to generate semantic similarity scores and categorical labels. Abstracts from journal PDFs are extracted and processed to determine similarity levels across four predefined categories. The optimal number of clusters was determined using Elbow Method, Silhouette Score, and Davies-Bouldin Index, resulting in k = 4. The system is implemented as a web-based application that allows users to upload two PDF files, compare them semantically, and receive both a similarity score and an AI-generated narrative explanation. Functional testing showed that all core features performed as expected. This system significantly reduces the time required to assess relatedness between journal articles, offering an efficient tool for academic research navigation.
IMPLEMENTASI K-MEANS DAN ANALISIS SENTIMEN KRITIK SARAN BERBASIS NLP PADA DATA MONEV BBPSDMP KOMINFO MAKASSAR Syahril Akbar; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Manual analysis of large-scale and unstructured textual feedback data is often inefficient and subjective, thereby hindering data-driven decision-making. This study aims to design and implement an integrated analytical workflow to automatically filter, cluster, and classify feedback data consisting of criticisms and suggestions. The research employs a hybrid approach that begins with TF-IDF-based data filtering, followed by dimensionality reduction using Latent Semantic Analysis (LSA), and topic clustering through K-Means clustering optimized with the Silhouette Score. The resulting cluster labels are then used as training data to build a Multinomial Naive Bayes classification model. The results show that this workflow successfully identified two main thematic clusters, namely "Criticism and Expectations" and "Suggestions and Compliments", and the classification model achieved an overall accuracy of 91%. Although class imbalance affected the recall of the minority class (47%), the model demonstrated high precision (95%) for that class. It is concluded that this hybrid approach effectively transforms raw data into structured insights, and utilizing clustering results as training data is an efficient strategy for automating feedback categorization, providing a reliable tool for institutional analysis.
PREDIKSI PEMAKAIAN AIR BULANAN DI PDAM KECAMATAN TAMALATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Nur Annisa Syarifuddin; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Water consumption forecasting is a crucial aspect of efficient water resource management, particularly in urban areas with increasing demand. This study aims to predict the monthly water usage volume at the PDAM of Tamalate District using the Autoregressive Integrated Moving Average (ARIMA) method. The dataset consists of historical water usage data from January 2022 to December 2024, totaling 36 monthly observations. The analysis process includes stationarity testing using the Augmented DickeyFuller (ADF) test, model parameter identification through ACF and PACF plots, and performance evaluation using MAE, RMSE, and MAPE metrics. The results show that the best-performing model is ARIMA, which demonstrates high prediction accuracy, with a MAE of 26,049.80 m³, RMSE of 37,459.00 m³, and MAPE of 4.12%. This model is capable of generating predictions close to actual values and can be relied upon as a basis for PDAM’s water distribution planning. It is expected that this research will contribute to data-driven decision-making and support digital transformation in the public service sector.
IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Nur Alam; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

The evaluation of government training programs is often hindered by manual analysis of unstructured qualitative feedback, making the process inefficient and subjective. This study aims to implement and evaluate a sentiment classification model using a hybrid Lexicon-Based and Support Vector Machine approach to analyze participants’ perceptions of the Vocational School Graduate Academy training organized by BBPSDMP Kominfo Makassar, as well as to compare the performance of a standard SVM model with a model optimized using Particle Swarm Optimization. This quantitative research employs 2,313 unstructured review data, which undergo text preprocessing, initial lexicon-based labeling, and TF-IDF feature extraction before being classified using an SVM with an RBF kernel. The results show that the SVM model optimized with PSO consistently outperforms the standard model across all four evaluation aspects, with the most significant accuracy improvement observed in the instructor category from 84.71% to 89.02% and in the assessor category reaching 91.46%. PSO optimization has proven effective in enhancing the model’s ability to identify negative sentiments, which represent the minority class. The hybrid approach with PSO optimization is capable of producing a more accurate and balanced classification system, with practical implications as an objective automated evaluation tool.
Co-Authors . Darniati Abd Rahman, Aedah Abrah, Rezkytullah Ahmad Faisal Ahmad Fauzi Saifuddin Akbar DB, Andi Muhammad ALRASYID.S, NUR FUAD Andi Makbul Syamsuri Andi Muhammad Akbar DB arikal khairat Asdar, Muhammad Dasril Bakti, Rizki Yusliana Basyar, Muhammad Yusuf Chyquitha Danuputri Chyquitha Danuputri Danuputri, Chyquitha Danuputri, Chyquitha Danuputri, Chyquitha Darniati Dedi Setiawan Desi Anggraeni Desi Anggreani Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Fachrim Irhamna Rachman Fahrim Irhmna Rachman Fatimah Azzahra Fauzan Azhari Rahman Fauzi Ahmad Muda FUAD, NUR FUAD ALRASYID.S Hadawina Hadawina Haidul, Haidul Jihan Izzathul Mujidah Khadijah Kusumawardani, Nurul Lukman Lukman Lukman Lukman Anas LUKMAN ANAS Lukman Lukman Lukman Lukman Lukman, Lukman Muh Nur Aqsal Aminullah Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Khadafi Muhammad Syafaat S. Kuba Muhsin, Muh Arief Muliana Nisha, Khairun Nur Alam Nur Annisa Syarifuddin Nurnawaty Nurul Kusumawardani RAHMANIA Rahmania RAMLI, ANDI RAODATUL ADAWIYAH Rasyidi, Muhammad Fachri Reski Abbas Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Risal Salsabila, Ismi Sari, Reski Anugrah Sarina Sunarti Suriani Suriani Syahril Akbar Titik Khawa Abdul Rahman Titin Wahyuni Ulil Amri Usman, Ansharullah Patiroi Utama, Prengki Putra Wahid, Abd Rahman